As recognized in the FOA for sub-mSv CT, the increased CT utilization and the associated increase in population radiation dose motivates the development of dose reduction methods. Any meaningful strategies for dose reduction must be coupled with an analysis of image quality. However, the relationship between dose and image quality is complex due to dependencies on the imaging task and patient-specific characteristics. The pro- posed effort develops task-driven CT through hardware modifications that permit customizing the CT acquisition to the patient, and through a task-based performance prediction framework that is used to drive optimal dose utilization for specific imaging scenarios. Specifically, a novel lightweight and compact x-ray beam modulator capable of high dynamic range modulations and suitable for typical CT gantry rotation rates will be developed based on multiple aperture devices (MADs). This approach allows for a high degree of control over the spatial profile of the x-ray beam including flattening of fluence profile arriving at the detector nd region-of- interest (ROI) scanning. Actuation of these devices will be driven by an image quality plan based on a 3D scout volume and a task-based detectability framework that includes sophisticated models of the measurement physics, imaging task definitions, the human visual system, and the particular reconstruction approaches applied to the data. While dynamic beam modulation alone yields significant dose reductions, these advantages will be synergized with additional reductions through the use of advanced statistical reconstruction algorithms customized for beam modulated acquisitions. We hypothesize that a task-driven diagnostic CT scanner tailored to the specific imaging needs of the patient will provide large enough dose reductions that many body CT scanning scenarios can be driven to sub-mSv levels as targeted by this program announcement. The following Specific Aims are proposed to develop and investigate task-driven CT: 1) Develop dynamic beam modulation hardware for integration into diagnostic CT scanners. Design, characterize, and integrate the MAD modulators into CT acquisition systems. 2) Create a reconstruction framework for dynamically modulated CT acquisitions. Adapt both traditional and statistical reconstruction algorithms to beam modulated data. 3) Develop a performance prediction framework for dynamically modulated CT. A sophisticated physical model and task-based mathematical observer will be developed that predicts the shift-variant, patient-specific, and acquisition-dependent image quality. 4) Develop strategies for driving patient- and task-based beam modulations. Using image quality plans (including possibly shift-variant specification, e.g. ROI imaging) and the prediction framework prospectively design optimal beam modulations within dose constraints. 5) Assess patient- and task-specific beam-modulated CT. Evaluation outcome measures will include quantitative imaging performance metrics, absorbed dose measurements and dose maps based on Monte Carlo estimation, and an observer preference test using cadaver studies including relations to minimum dose protocols.